Leveraging Data Analytics in Transfer Pricing Audits


Ngozi Benita Onyebezie and Akaoma Osele of KPMG look at the potential for the use of technology in transfer pricing audits, with a focus on Nigeria. They also discuss some of the challenges and obstacles encountered by multinational enterprises when attempting to incorporate data analytics into company operations.

In the last few years the field of transfer pricing (TP) in Nigeria has witnessed the introduction of e-filing platforms, automated documentation tools and a commendable shift from paper-based compliance to the acceptance of digitally presented compliance submissions by the tax authorities. The coronavirus pandemic that has rocked the world since 2020 also highlighted the adaptability of both tax consultants and tax authorities, as e-meetings and submissions became the norm.

However, one area where technology can be better leveraged in Nigeria is in TP audits. In the last year, many taxpayers in Nigeria, particularly those operating as part of a multinational enterprise (MNE), have received letters from the tax authorities requesting information and documents relating to their transactions with related parties. These letters indicate that such companies have been selected for a TP audit, which essentially checks that a company’s related-party transactions have been conducted in compliance with the relevant regulations and that the level of profit reported is consistent with comparable enterprises.

This article discusses the TP audit process and how technology can be leveraged by both MNEs and tax authorities to enhance the process and results. It provides a realistic and thorough solution to the incorporation of data analytics into TP audits by outlining the measures that need to be taken to render this a possibility.

This article also addresses some of the obstacles encountered by MNEs when attempting to incorporate data analytics into company operations, and aims to raise awareness of data analytics among taxpayers, TP professionals, and tax authorities.
TP Audit Process and Data Analytics

Data analytics involves the use of analytical instruments and techniques to derive information and to convey empirical conclusions to unresolved questions raised by the nature of structured and unstructured data. The rapid growth of data in the world today has meant that the incorporation of data analytics into any business function is crucial.

Data analysis in the tax field blends tax technological expertise and emerging technologies, notably in the fields of artificial intelligence and machine learning, with a large number of data to provide practical insight.

With regard to TP audits, data analytics can be applied to enhance the process and lead to a quicker achievement of audit objectives.

The objective of the tax authorities in conducting a TP audit is to identify related-party transactions that have been structured to either shift profits to jurisdictions with lower or zero tax rates (tax havens) or erode the local country’s tax base. Where such transactions are discovered, TP adjustments are made.

However, the process of reaching this conclusion is far from simple. TP audits are time- and resource-intensive and require a multidisciplinary team of auditors, review of several documents and internal records, site visits, analysis of large volumes of financial and economic data, industry research, an understanding of the company’s business, and negotiation with the taxpayer.

Data analytics can undoubtedly simplify some of the above processes and ensure speedy conclusions of audits—which, in Nigeria, can take upwards of two years to conclude.

The following sections analyze the TP audit process and data analytics solutions that can be incorporated into the identified processes.
Risk Assessment

This is the fact-finding phase that lays the foundation for the TP audit. It seeks to identify elements in the MNE’s TP profile that suggest the presence of TP risks. The objective of this activity is to determine whether the audit target has material controlled transactions, whether there are any indications of TP risk, and whether the case is worth the time and resource investment required for an audit. At this point, it is necessary to ask the right questions, get the facts right and do so promptly.

The emergence of big data, machine learning and artificial intelligence, among other technological advances, provides more possibilities for the task of extracting data from multi-variate sources to a single data warehouse where this data can then be made available to users. For example, this stage can be enhanced with the development of a text reader fortified through artificial intelligence that will be able to read texts from PDF, Microsoft Word, HTML, CSV and other files, which will then be summarized and reported to the user of the information.

The system then goes on to give a rating to the document based on algorithms that would have been written into the software system at the backend. For example, this tool will be enabled to read a TP documentation report and assign a rating of 1–3 or 1–5 to it, based on the parameters that would have been encoded into the system.

Although there are some tools available for the testing of risk assessment, such as the African Tax Administration Forum risk assessment tools, there is room for more development in this regard.
Pre-Audit Phase

This stage typically involves the information and documents request by the tax authorities. As soon as the tax authorities have determined that there is a TP risk, the next step is to write a formal letter requesting certain documents to be sent to the tax authorities.

In our experience, this letter triggers the commencement of the audit process from the taxpayer’s point of view. Usually, these letters are sent to the TP advisers of the MNEs, who then initiate the process of gathering the documents and ensuring that they are thoroughly reviewed for consistency before submitting them to the tax authorities.

The data analytics steps relevant at this stage of the TP audit include data collection, data storage, data mining and quarrying techniques. The information documentation process previously involved considerable paperwork and hard copy documents which would then be submitted to the tax office. in recent years, however, sending information electronically via emails and USB drives has become more popular.

Data analytics has made it easier to collect necessary data from multiple sources, gather the data into a standard database, and clean the data using data collection, cleaning, and gathering techniques. Online forms, Power Excel, location data, and a variety of other applications can all be used for this process.
Field Audit Phase

After a review of the initial documents submitted by the company, the tax authorities proceed to notify the taxpayer of their intention to visit the company for a field audit via a formal letter. The audit phase is mostly made up of physical site visits, meetings with the company’s officers, and interview sessions with key personnel directly involved in the conduct of related party transactions.

The interviews are carried out to gain insights into some issues that the tax authorities might not have obtained answers to from the execution of the pre-audit stage. During this phase, the taxpayer may also be required to make a presentation on its business structure, operations and related party arrangements.

The use of speech-to-text devices for these interviews will go a long way in reducing time spent on handwritten interview transcripts, and will improve accuracy. For presentations, the analytics tools that are likely to aid performance are mostly visualization techniques using tools like Tableau, and Power BI in presentations that can be easily understood by the tax authorities.

The MNE under audit presents to the tax authorities its defense for the TP methodology applied in the analysis of its related-party transactions, or for any pricing strategy that it may have adopted, with concrete reasons for adopting the stated method or strategy.

In certain situations, a face-to-face conversation would be more convenient and productive at this point. However, the current pandemic has made the world more aware of how effective the alternative means are, and how things can be effectively done without a face-to-face meeting—video and teleconferencing apps are being utilized successfully.

Further, the tax authorities may explore the use of drones for tours/inspection of physical locations like warehouses, rig yards, and office premises.
Post-Field Audit

This stage includes the issuance of a TP audit report by the tax authorities, the TP audit assessment, the TP audit reconciliation, and TP litigation, where applicable.

The TP audit report is the final opinion of the tax authorities regarding the TP audit. This report is usually prepared by the tax authorities and sent to the MNE for review, after which the audit is finalized.

The TP assessment is the tax authorities’ opinion of what the true profit of the MNE is, and how much tax the MNE should pay as income tax against what has been documented by the MNE. This position may be based on the tax authorities’ findings with regard to the reliability of the benchmarking studies or other factors.

To demonstrate how they arrived at the additional tax liability to be paid by the MNE, tax officials could use accurate data analytics models such as regression analysis. Often, the TP assessment stage gives rise to TP adjustments and is met with objections by the MNE. At the point of TP adjustments, the tax authorities and taxpayers try to meet on common ground concerning findings reached by the tax authorities.

TP audit reconciliation and litigation are the last stages. After the reconciliation of the audit report between the tax authorities and the MNE, the parties involved may or may not reach an agreement. Where an agreement is reached, the audit is settled and the MNE pays any resulting additional tax liabilities.

However, where no agreement has been reached, the issue is escalated to the courts for a ruling on the matter. The first TP case in Nigeria was decided in 2019 in a case between Prime Plastichem Nigeria Limited and the Federal Inland Revenue Service (FIRS), which was ruled in favor of the FIRS.

An innovative solution would be to design a data warehouse for TP cases. This would double as a TP library for taxpayers and tax officials to refer to when attempting to settle TP audit issues.
Leveraging Data Analytics Solutions in Preparing for TP Audit

The best way to prepare for a TP audit is to fully incorporate the requirements of the Nigerian Transfer Pricing Regulations, 2018, in tax operations, with the goal of achieving full compliance with the Regulations. The MNE is expected to have an up-to-date TP policy, prepare its TP documentation when due, and file its tax and TP returns on time. Further, an internal assessment of risk areas is necessary to ensure that remedial actions are taken in advance where possible.

Data analytics can be a big help in these processes. Some examples of TP audit risk areas that may be effectively addressed with data analytics solutions are discussed below.
Low Profitability Levels

Consecutive reporting of loss when other companies in the same industry, under similar circumstances, are reporting profits, is an indicator of possible transfer mis-pricing and is one of the reasons the tax authorities will scrutinize the related party transactions of an MNE.

The use of a reporting system developed by machine learning and artificial intelligence software that shows a trend of the possible contributing factors to the loss being reported in the business will advise the MNE on the internal and external factors affecting the business; which may contribute to a near resolution of the problem by the MNE or at least provide tangible explanations that confirm such losses are caused by factors unrelated to TP arrangements with affiliates.
Inconsistency in Internal Records

It is expected that information reported in the tax/TP returns of the MNE as well as in its TP documentation be consistent with the financial statements, related-party transactions ledgers, trial balance, general ledgers, etc. Major inconsistencies may also trigger more inquiry by the tax authorities.

A database system that links all books of account for an MNE can be employed under this point. This system does not require the hard coding of artificial intelligence. A tool that is as readily accessible as Excel will meet the requirements of aligning all financial data for consistency.
Significant Transactions with Affiliates in Tax Haven Jurisdictions

This may suggest that there has been a deliberate effort by the MNE to attribute more profits to such jurisdictions. All affiliate activities, transaction contracts, relationship status of all entities within an MNE group, including details of their jurisdictions and corporate tax rates, should be duly recorded online using cloud-based analytics systems, and offline using spreadsheets, to guide the tax manager of the MNE in the isolation of higher risk transactions.
Practical Challenges Affecting the Application of Data Analytics in TP Audit

As TP audits become more common and data analytics technologies continue to make more breakthroughs, the challenges faced by MNEs in the use of data analytics in their functions are also on the rise. The following sections discuss the practical issues confronting the implementation of data analytics for the management of TP audits.
Data Privacy and Confidentiality

Data confidentiality is one of the major issues confronting taxpayers. Organizations may need to differentiate between what data is important for tax assessment purposes, and what data is strictly confidential and should not be shared. Typically, high-level programming languages like R and Python are used for the extraction of data from a database. These are strictly computer programming languages that the tax manager of the MNE might not be conversant with, hence the need to employ a database administrator.

Also, data-gathering activities like printing, pasting, and downloading of significant volumes of client data can infringe on the confidentiality agreements of the MNE.

Other challenges include data stored in the cloud, cloud sharing of data, updates to data protection, and the lack of technical expertise in data management.
Subjective Nature of TP Audits

The subjective nature of TP audits puts in doubt the possibility of full implementation of data analytics methods and techniques. While some might argue that data analytics, objective as it is, cannot be entirely included in the conduct of a TP audit, others might claim that an 80:20 solution is sufficient.
Data Retention

Large volumes of data are typically requested by the tax authorities for audit purposes. TP audits usually cover transactions that occurred within a specific time frame, normally not exceeding six years. However, most data management solutions maintain business data over several years, depending on when the organization’s information infrastructure was built up.

Although this is not necessarily a negative point, the expense of maintaining high-level data contributes to organizational expenses, sluggish database development, and may also result in data mixing, in the sense that, within a group, data on different entities may become mixed up.

For other companies, newly installed database management systems may mean that data for previous periods cannot be retrieved for TP audit purposes, or at best are only available in physical copies which may be incomplete, damaged or even lost.
Data Synchronization

MNE data may be stored in different locations and vary from out-of-date to newly developed systems, from which all information to be sent to the tax authorities needs to be retrieved. As a result, costs for converting data sets into compatible formats on which analysis can be performed may arise. Similarly, data sets may have a higher level of reliability if they come from a system for which internal controls are operating effectively.
Personnel and Cost Requirements

Data analytics is a highly technical and ever-evolving discipline. Thus, the responsible personnel must have the necessary expertise required to perform the functions. This calls for significant investment in the training and retraining of the employees that will take up the management of database systems, benchmarking, and financial analysis of the related-party transactions that may eventually be subject to a TP audit.
Going Forward

Despite their laudable objective of protecting the nation’s tax base and increasing compliance with established TP Regulations, TP audits are generally time- and resource-consuming for both taxpayers and the tax authorities.

Resolution of identified issues usually takes a considerable time, owing to the volume of data to be processed and reviewed by the tax authorities. Taxpayers are often stretched to the limit as they must shift focus and attention from their core operations to responding to the seemingly unending information requests of tax authorities.

While data analytics can greatly enhance the TP audit process and increase efficiency, this is not without its challenges. It is incumbent on tax authorities and MNEs to continue evaluating their processes and demonstrate agility in adapting new technologies to achieve the intended objectives.

This column does not necessarily reflect the opinion of The Bureau of National Affairs, Inc. or its owners.

Ngozi Benita Onyebezie is Transfer Pricing Manager and Akaoma Osele is Semi-Senior Adviser with KPMG Nigeria.

The authors may be contacted at: ngozi.onyebezie@ng.kpmg.com; akaoma.osele@ng.kpmg.com